한국전자거래학회지 (The Journal of Society for e-Business Studies)

  • 제23권3호
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  • Pages.207-224
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  • 2018
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  • 2288-3908(pISSN)
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  • 2765-3846(eISSN)
한국전자거래학회 (Society for e-Business Studies)
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인공지능기법을 이용한 온라인 P2P 대출거래의 채무불이행 예측에 관한 실증연구

Artificial Intelligence Techniques for Predicting Online Peer-to-Peer(P2P) Loan Default

  • Bae, Jae Kwon (Dept. of Management Information Systems, Keimyung University) ;
  • Lee, Seung Yeon (Dept. of Statistics, Keimyung University) ;
  • Seo, Hee Jin (Dept. of Management Information Systems, Keimyung University)
  • 투고 : 2018.07.25
  • 심사 : 2018.08.17
  • 발행 : 2018.08.31
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초록

온라인 P2P 대출(Online Peer-to-Peer Lending)이란 대출자(차입자)들이 인터넷 및 모바일 P2P 플랫폼을 통해 대출을 신청하면 P2P 플랫폼 기업이 이를 심사하고, 공개하여 불특정 다수가 자금을 빌려주고 이자를 받는 대출중개 서비스를 말한다. 국내외적으로 P2P 대출시장의 성장과 수익률에 대한 관심이 커진 상황에서 현재는 P2P 대출에 대한 안정성 측면에서 문제가 제기되고 있다. P2P 대출시장은 높은 수익률을 제공하지만 P2P 업체의 연체율과 부실률(채무불이행률)도 함께 높아지고 있는 실정이다. P2P 금융시장의 신뢰도를 높이기 위해서는 P2P 대출의 연체율과 채무불이행률을 줄이는 것이 무엇보다 중요하다. 본 연구는 세계적인 P2P 기업인 렌딩클럽(Lending Club)의 P2P 대출거래데이터베이스를 이용하여 인공지능기반의 P2P 채무불이행 예측모형을 구축하고자 한다. 구체적으로 벤치마크(benchmark) 모형으로 통계기법인 판별분석과 로지스틱 회귀분석을 이용하고, 인공지능기법으로는 신경망, CART, 그리고 C5.0을 이용하여 P2P 대출거래의 채무불이행 예측모형을 구축하고자 한다. 연구결과, P2P 대출거래의 채무불이행 예측을 위해 우선 고려해야 할 변수는 대출이자율이며, 중요도 3순위에 가장 많이 언급된 대출금액과 총부채상환비율도 고려해야 할 요인으로 추출되었다. 전통적인 통계기법보다는 인공지능기법의 예측성과가 더 좋은 것으로 나타났으며, 신경망의 경우 모든 데이터 셋에서 오분류율이 가장 낮은 예측모형으로 나타났다.

In this article, an empirical study was conducted by using public dataset from Lending Club Corporation, the largest online peer-to-peer (P2P) lending in the world. We explore significant predictor variables related to P2P lending default that housing situation, length of employment, average current balance, debt-to-income ratio, loan amount, loan purpose, interest rate, public records, number of finance trades, total credit/credit limit, number of delinquent accounts, number of mortgage accounts, and number of bank card accounts are significant factors to loan funded successful on Lending Club platform. We developed online P2P lending default prediction models using discriminant analysis, logistic regression, neural networks, and decision trees (i.e., CART and C5.0) in order to predict P2P loan default. To verify the feasibility and effectiveness of P2P lending default prediction models, borrower loan data and credit data used in this study. Empirical results indicated that neural networks outperforms other classifiers such as discriminant analysis, logistic regression, CART, and C5.0. Neural networks always outperforms other classifiers in P2P loan default prediction.

키워드

KJGRBH_2018_v23n3_207_f0001.png 이미지

Analysis Procedures

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An Example of CART Rules from Training Dataset 1

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An Example of C5.0 Rules from Training Dataset 1

Cumulative Amount and Default Rate of P2P Loan Decisions

KJGRBH_2018_v23n3_207_t0001.png 이미지

Loan Application Status of Lending Club(Jan 2016 to Dec 2017)

KJGRBH_2018_v23n3_207_t0002.png 이미지

Variable Used in the Study

KJGRBH_2018_v23n3_207_t0003.png 이미지

Maximum Tree Depth and Splitting Criterion of Decision Trees

KJGRBH_2018_v23n3_207_t0004.png 이미지

The Determinant Factors for Predicting P2P Lending Default

KJGRBH_2018_v23n3_207_t0005.png 이미지

Misclassification Rate of P2P Lending Default Prediction Models

KJGRBH_2018_v23n3_207_t0006.png 이미지

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